Nonlinear adaptive RBFNN control of a one-link flexible manipulator

This paper describes a hybrid approach to the problem of controlling flexible link manipulators in the face of both structured and unstructured uncertainties. First, a nonlinear controller based on the equations of motion of the robot is elaborated. Its aim is to produce a stable control. Then, an adaptive RBF neural controller is implemented to compensate structured and unstructured uncertainties. Efficiency of the new controller obtained by combining the two control laws is tested facing an important variation of the dynamic parameters of the flexible manipulator and compared to a classical nonlinear controller. Simulation results show the effectiveness of the proposed control strategy.